A neuro-money recognition using optimized masks by GA
Up to now, much research of the application to neural networks (NN) has been reported. We have proposed a neuro-pattern recognition for bill money with masks and have reported its effectiveness for money recognition. Recently, genetic algorithm (GA) is reported as the effective optimizing method. In this paper, we adopt the GA to mask optimization in the recognition method. Namely, we regard the position of the masked part in the input image as a gene. We operate crossover, selection, and mutation to some genes. By repeating a series of these operations, we can get effective masks for paper currency recognition. We compare the ability of NN using the optimized masks by the GA with the one of NN using the random masks determined by random numbers. Then we show that the GA is effective to optimize masks for the method of neuro-pattern recognition with masks. Furthermore, we develop high-speed neuro-recognition board to realize the neuro-pattern recognition for paper currency in the commercial products.
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